The \(\text{BiMoNN}\)  is fully differentiable, and we learn its parameters with classical deep learning techniques. Let \(\mathcal {L}: \Omega _I^2 \rightarrow \mathbb {R}\)  be a differentiable loss function. Depending on our interpretation of the outside domain \(\mathbb {Z}^d ~\backslash ~ \Omega _I\) , the effects on the border can vary. Thus, the loss is computed by avoiding the borders, whose size is half of the kernel size \(\Omega _S\)  in each direction. We try classical segmentation losses: the dice loss [1]}, the binary cross-entropy (BCE), and the mean squared error (MSE).
